|
|
|
| Short term photovoltaic power prediction in high-altitude and cold regions based on numerrical weather prediction factor expansion and improved ensemble learning |
| LIU Wei, YANG Kaining |
| School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, Heilongjiang 163000 |
|
|
|
|
Abstract Due to meteorological conditions, photovoltaic systems in high-altitude and cold regions exhibit more significant fluctuations in their photovoltaic power. This article takes a photovoltaic power station in Heilongjiang as an example and proposes a short-term photovoltaic power prediction method for high-altitude and cold regions based on numerical weather prediction (NWP) factor expansion and improved conventional Stacking ensemble learning. In response to the large fluctuations in photovoltaic power in high-altitude and cold regions, the NWP differential factor is introduced as a cross feature to enhance the sensitivity of the model to weather changes. Subsequently, an ensemble learning model is constructed using extreme gradient boosting (XGBoost) and long short term memory (LSTM) network as base learners, and temporal convolutional network (TCN) as meta learners, and the model structure is optimized using forward validation. Finally, comparative experimental analysis is conducted, and the results show that the proposed method has higher prediction accuracy and stability.
|
|
Received: 02 April 2024
|
|
|
|
| Cite this article: |
|
LIU Wei,YANG Kaining. Short term photovoltaic power prediction in high-altitude and cold regions based on numerrical weather prediction factor expansion and improved ensemble learning[J]. Electrical Engineering, 2024, 25(8): 1-10.
|
|
|
|
| URL: |
|
http://dqjs.cesmedia.cn/EN/Y2024/V25/I8/1
|
[1] 龚莺飞, 鲁宗相, 乔颖, 等. 光伏功率预测技术[J].电力系统自动化, 2016, 40(4): 140-151.
[2] 国家能源局. 国家能源局发布2023年全国电力工业统计数据[J]. 电力科技与环保, 2024, 40(1): 95.
[3] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
[4] 贾凌云, 云斯宁, 赵泽妮, 等. 神经网络短期光伏发电预测的应用研究进展[J]. 太阳能学报, 2022, 43(12): 88-97.
[5] 刘琼, 田晓梨, 花凌锋, 等. 基于G-L-R模型的光伏发电功率预测[J]. 电气技术, 2020, 21(8): 11-15, 21.
[6] 陈振祥, 林培杰, 程树英, 等. 基于K-means++和混合深度学习的光伏功率预测[J]. 电气技术, 2021, 22(9): 7-13, 33.
[7] 时珉, 许可, 王珏, 等. 基于灰色关联分析和GeoMAN模型的光伏发电功率短期预测[J]. 电工技术学报, 2021, 36(11): 2298-2305.
[8] 刘晓艳, 王珏, 姚铁锤, 等. 基于卫星遥感的超短期分布式光伏功率预测[J]. 电工技术学报, 2022, 37(7): 1800-1809.
[9] 吴珺玥, 赵二刚, 郭增良, 等. 基于Spearman系数和TCN的光伏出力超短期多步预测[J]. 太阳能学报, 2023, 44(9): 180-186.
[10] 李智, 丁津津, 陈凡, 等. 计及机理机制的Stacking集成光伏发电预测[J]. 科学技术与工程, 2023, 23(19): 8212-8217.
[11] 张姗, 冬雷, 纪德洋, 等. 基于NWP相似性分析的超短期光伏发电功率预测[J]. 太阳能学报, 2022, 43(4): 142-147.
[12] 唐波, 岳喜稳, 朱瑞金, 等. 多策略优化的长短时记忆网络短期多维时序光伏功率预测[J/OL]. 电力系统及其自动化学报, 1-12 [2024-04-03].https://doi.org/10.19635/j.cnki.csu-epsa.001416. |
|
|
|
2024, Vol. 25 
